Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

Guerra, Luis; McGarry, Laura M.; Robles Forcada, Víctor; Bielza, Concha; Larrañaga Múgica, Pedro y Yuste, Rafael (2010). Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study. "Developmental Neurobiology", v. 71 (n. 1); pp. 71-82. ISSN 1932-8451. https://doi.org/10.1002/dneu.20809.

Descripción

Título: Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study
Autor/es:
  • Guerra, Luis
  • McGarry, Laura M.
  • Robles Forcada, Víctor
  • Bielza, Concha
  • Larrañaga Múgica, Pedro
  • Yuste, Rafael
Tipo de Documento: Artículo
Título de Revista/Publicación: Developmental Neurobiology
Fecha: Diciembre 2010
Volumen: 71
Materias:
Palabras Clave Informales: supervised;classification;clustering;pyramidal cell;interneuron
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies.

Más información

ID de Registro: 7284
Identificador DC: http://oa.upm.es/7284/
Identificador OAI: oai:oa.upm.es:7284
Identificador DOI: 10.1002/dneu.20809
URL Oficial: http://onlinelibrary.wiley.com/doi/10.1002/dneu.20809/suppinfo
Depositado por: Memoria de Investigacion 2
Depositado el: 30 May 2011 09:14
Ultima Modificación: 20 Abr 2016 16:27
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